Requirements

Experience leading data science or ML teams with production systems impacting commercial metrics.
Hands-on skills in SQL, Python, feature engineering, model evaluation, and deploying systems.
Knowledge of both traditional ML (propensity, uplift models, feature pipelines) and agentic systems (tools, prompt iteration, evals, observability).
Experience running live systems including monitoring, retraining, incident handling, and rollback.
Pragmatic approach to methods, using rules or heuristics where appropriate and reserving models/agents for genuine value.
Ability to operate with an undefined starting point, forming and defending a prioritization view based on data.
Strong measurement skills including experimentation, uplift modeling, back-testing, and financial translation of impact.
Fluency in production ML engineering: training pipelines, model registries, inference services, feature stores, drift detection, traceability.
Ability to hire, level, and develop senior data scientists and ML engineers and establish professional standards.
Credibility with executives and commercial stakeholders to prioritize high-value work and decline low-value tasks.
Experience in payments, fintech, subscriptions, regulated transactional domains to quickly estimate credible value.
Comfortable with regulated ML as a design constraint, managing model risk, DPIAs, auditability, and traceability.

What you'll be doing

Prioritize and build a value-based backlog of data science use cases in payments, marketing, growth, risk, and compliance.
Personally deliver the first automated decisioning system end-to-end including analysis, model building, deployment, and testing.
Operate deployed systems including monitoring, retraining, drift handling, incidents, and rollbacks in collaboration with engineering.
Work across traditional ML and agentic systems to select the best approach for each problem.
Build and lead a hub-and-spoke team of data scientists and ML engineers embedded in value areas of the business.
Establish the production stack with Data Platform and Engineering for reproducible training, model registries, inference, feature versioning, eval harnesses, and monitoring.
Own end-to-end value capture with shadow and A/B testing, financial metric translation, and quarterly reporting.
Define governance models for automated decisioning including risk assessment, ownership, latency, escalation, compliance, and GDPR/AI Act adherence.
Clarify roles and boundaries across Product Science, Analytics Engineering, Data Platform, and AI Enablement.
Deliver cross-functionally by embedding in delivery teams with product owners, domain experts, and engineers.

Experience in regulated transactional domains such as payments or fintech.
Familiarity with compliance regulations including GDPR and the EU AI Act related to automated decisioning.
Experience balancing rule-based heuristics with automated models to maximize value.

Attractive salaries and stock options.
Unlimited holidays and enhanced parental leave.
Retirement plans and private healthcare.
Well-being initiatives and a learning fund for personal development.
Regular internal and external training opportunities.
Flexible digital-first work culture with autonomy over when, where, and how to work.
Global hubs for local community and focused collaboration in UK, Portugal, Netherlands, Ireland, USA, Canada, and Singapore.
Inclusive and diverse culture welcoming all individuals and underrepresented groups.
Commitment to accommodating applicants for a smooth application process and supportive workplace.